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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Statistical Methods in Clinical Trials
Retraction
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,557 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,557 works in the cohort · of 4,299,418page 2 of 32

Labels cover 46 of 1,557 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,557 of 1,557 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

venueno affunlabeled
Cure rate models: A unified approach
Guosheng Yin, Joseph G. Ibrahim
2005· article· en· Canadian Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
170
citations
affunlabeled
Speeding up the Evaluation of New Agents in Cancer
Mahesh Parmar, F. M.- S. Barthel, Matthew R. Sydes, Ruth E. Langley, Richard Kaplan, E. Eisenhauer +6 more
2008· article· en· JNCI Journal of the National Cancer Institute· Mathematics
machine prediction:candidate · metaresearchconsensus · none
150
citations
venueno affunlabeled
How to Randomize
Andrew J. Vickers
2006· review· en· Journal of the Society for Integrative Oncology· Mathematics
machine prediction:candidate · metaresearchconsensus · none
150
citations
affunlabeled
Combining <i>p</i>-values via averaging
Vladimir Vovk, Ruodu Wang
2020· article· en· Biometrika· Mathematics
machine prediction:candidate · metaresearchconsensus · none
149
citations
fundno affunlabeled
Sequential Selection Procedures and False Discovery Rate Control
Max G’Sell, Stefan Wager, Alexandra Chouldechova, Robert Tibshirani
2015· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Mathematics
machine prediction:candidate · noneconsensus · none
146
citations
affno abstractunlabeled
The history and development of N-of-1 trials
R.D. Mirza, Salima Punja, Sunita Vohra, Gordon Guyatt
2017· article· en· Journal of the Royal Society of Medicine· Mathematics
machine prediction:candidate · metaresearchconsensus · metaresearch
146
citations
affunlabeled
Composite End Points in Clinical Research
Paul W. Armstrong, Cynthia M. Westerhout
2017· review· en· Circulation· Mathematics
machine prediction:candidate · metaresearchconsensus · none
132
citations
affunlabeled
Improved up‐and‐down designs for phase I trials
Anastasia Ivanova, A. Montazer-Haghighi, Sri Gopal Mohanty, S. D. Durham
2002· article· en· Statistics in Medicine· Mathematics
machine prediction:candidate · metaresearchconsensus · none
130
citations
affno abstractunlabeled
Towards new methods for the determination of dose limiting toxicities and the assessment of the recommended dose for further studies of molecularly targeted agents – Dose-Limiting Toxicity and Toxicity Assessment Recommendation Group for Early Trials of Targeted therapies, an European Organisation for Research and Treatment of Cancer-led study
Sophie Postel‐Vinay, Laurence Collette, Xavier Paolettí, Elisa Rizzo, Christophe Massard, David Olmos +11 more
2014· article· en· European Journal of Cancer· Mathematics
machine prediction:candidate · metaresearchconsensus · metaresearch
122
citations
affunlabeled
Do we need to adjudicate major clinical events?
Christopher B. Granger, Victor G. Vogel, Steve Cummings, Peter Held, Fred T. Fiedorek, Mitzi Lawrence +6 more
2008· review· en· Clinical Trials· Mathematics
machine prediction:candidate · metaresearchconsensus · metaresearch
120
citations
affno abstractunlabeled
Fundamentals of Population Pharmacokinetic Modelling
Catherine M.T. Sherwin, Tony K. L. Kiang, Michael G. Spigarelli, Mary H. H. Ensom
2012· review· en· Clinical Pharmacokinetics· Mathematics
machine prediction:candidate · noneconsensus · none
116
citations
affunlabeled
Critical concepts in adaptive clinical trials
Jay Park, Kristian Thorlund, Edward J. Mills
2018· review· en· Clinical Epidemiology· Mathematics
machine prediction:candidate · metaresearchconsensus · none
99
citations

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